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conf/AGCRN/CA_District5.conf

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+[data]
+num_nodes = 211
+lag = 12
+horizon = 12
+val_ratio = 0.1
+test_ratio = 0.2
+tod = False
+normalizer = std
+column_wise = False
+default_graph = True
+
+[model]
+input_dim = 1
+output_dim = 1
+embed_dim = 2
+rnn_units = 64
+num_layers = 2
+cheb_order = 2
+
+[train]
+loss_func = mae
+seed = 10
+batch_size = 64
+epochs = 100
+lr_init = 0.003
+lr_decay = False
+lr_decay_rate = 0.3
+lr_decay_step = 5,20,40,70
+early_stop = True
+early_stop_patience = 15
+grad_norm = False
+max_grad_norm = 5
+real_value = True
+
+[test]
+mae_thresh = None
+mape_thresh = 0.
+
+[log]
+log_step = 20
+plot = False

conf/AGCRN/NYC_BIKE.conf

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+[data]
+num_nodes = 250
+lag = 12
+horizon = 12
+val_ratio = 0.2
+test_ratio = 0.2
+tod = False
+normalizer = std
+column_wise = False
+default_graph = True
+
+[model]
+input_dim = 2
+output_dim = 2
+embed_dim = 2
+rnn_units = 64
+num_layers = 2
+cheb_order = 2
+
+[train]
+loss_func = mae
+seed = 12
+batch_size = 64
+epochs = 100
+

conf/AGCRN/PEMS07M.conf

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+[data]
+num_nodes = 228
+lag = 12
+horizon = 12
+val_ratio = 0.1
+test_ratio = 0.2
+tod = False
+normalizer = std
+column_wise = False
+default_graph = True
+
+[model]
+input_dim = 1
+output_dim = 1
+embed_dim = 2
+rnn_units = 64
+num_layers = 2
+cheb_order = 2
+
+[train]
+loss_func = mae
+seed = 10
+batch_size = 64
+epochs = 100
+lr_init = 0.003
+lr_decay = False
+lr_decay_rate = 0.3
+lr_decay_step = 5,20,40,70
+early_stop = True
+early_stop_patience = 15
+grad_norm = False
+max_grad_norm = 5
+real_value = True
+
+[test]
+mae_thresh = None
+mape_thresh = 0.
+
+[log]
+log_step = 20
+plot = False

conf/AGCRN/chengdu_didi.conf

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+[data]
+num_nodes = 524
+lag = 12
+horizon = 12
+val_ratio = 0.1
+test_ratio = 0.2
+tod = False
+normalizer = std
+column_wise = False
+default_graph = True
+
+[model]
+input_dim = 1
+output_dim = 1
+embed_dim = 2
+rnn_units = 64
+num_layers = 2
+cheb_order = 2
+
+[train]
+loss_func = mae
+seed = 10
+batch_size = 64
+epochs = 100
+lr_init = 0.003
+lr_decay = False
+lr_decay_rate = 0.3
+lr_decay_step = 5,20,40,70
+early_stop = True
+early_stop_patience = 15
+grad_norm = False
+max_grad_norm = 5
+real_value = True
+
+[test]
+mae_thresh = None
+mape_thresh = 0.
+
+[log]
+log_step = 20
+plot = False

conf/ASTGCN/CA_District5.conf

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+[data]
+num_nodes = 211
+len_input = 12
+num_for_predict = 12
+
+[model]
+nb_block = 2
+K = 3
+nb_chev_filter = 64
+nb_time_filter = 64
+time_strides = 1

conf/ASTGCN/NYC_BIKE.conf

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+[data]
+num_nodes = 250
+len_input = 12
+num_for_predict = 12
+
+[model]
+nb_block = 2
+K = 3
+nb_chev_filter = 64
+nb_time_filter = 64
+time_strides = 1

conf/ASTGCN/PEMS07M.conf

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+[data]
+num_nodes = 228
+len_input = 12
+num_for_predict = 12
+
+[model]
+nb_block = 2
+K = 3
+nb_chev_filter = 64
+nb_time_filter = 64
+time_strides = 1

conf/ASTGCN/chengdu_didi.conf

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+[data]
+num_nodes = 524
+len_input = 12
+num_for_predict = 12
+
+[model]
+nb_block = 2
+K = 3
+nb_chev_filter = 64
+nb_time_filter = 64
+time_strides = 1

conf/FlashST/Params_pretrain.py

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+import argparse
+# import numpy as np
+import configparser
+# import pandas as pd
+
+def parse_args(device):
+    # parser
+    args = argparse.ArgumentParser(prefix_chars='-', description='pretrain_arguments')
+    args_get, _ = args.parse_known_args()
+    # get configuration
+    config_file = '../conf/FlashST/config.conf'
+    config = configparser.ConfigParser()
+    config.read(config_file)
+
+    # parser
+    args = argparse.ArgumentParser(prefix_chars='-', description='arguments')
+
+    args.add_argument('-cuda', default=True, type=bool)
+    args.add_argument('-device', default=device, type=str, help='indices of GPUs')
+    args.add_argument('-mode', default='ori', type=str, required=True)
+    args.add_argument('-model', default='STGCN', type=str)
+    args.add_argument('-dataset_test', default='PEMS07M', type=str)
+    args.add_argument('-dataset_use', default=config['data']['dataset_use'].split(','))
+
+    # data
+    args.add_argument('-his', default=config['data']['his'], type=int)
+    args.add_argument('-pred', default=config['data']['pred'], type=int)
+    args.add_argument('-val_ratio', default=config['data']['val_ratio'], type=float)
+    args.add_argument('-test_ratio', default=config['data']['test_ratio'], type=float)
+    args.add_argument('-tod', default=config['data']['tod'], type=eval)
+    args.add_argument('-normalizer', default=config['data']['normalizer'], type=str)
+    args.add_argument('-column_wise', default=config['data']['column_wise'], type=eval)
+    args.add_argument('-default_graph', default=config['data']['default_graph'], type=eval)
+    # model
+    args.add_argument('-input_base_dim', default=config['model']['input_base_dim'], type=int)
+    args.add_argument('-input_extra_dim', default=config['model']['input_extra_dim'], type=int)
+    args.add_argument('-output_dim', default=config['model']['output_dim'], type=int)
+    args.add_argument('-node_dim', default=config['model']['node_dim'], type=int)
+    args.add_argument('-embed_dim', default=config['model']['embed_dim'], type=int)
+    args.add_argument('-num_layer', default=config['model']['num_layer'], type=int)
+    args.add_argument('-temp_dim_tid', default=config['model']['temp_dim_tid'], type=int)
+    args.add_argument('-temp_dim_diw', default=config['model']['temp_dim_diw'], type=int)
+    args.add_argument('-use_lpls', default=config['model']['use_lpls'], type=eval)
+    args.add_argument('-if_time_in_day', default=config['model']['if_time_in_day'], type=eval)
+    args.add_argument('-if_day_in_week', default=config['model']['if_day_in_week'], type=eval)
+    args.add_argument('-if_spatial', default=config['model']['if_spatial'], type=eval)
+    # train
+    # args.add_argument('-mode', default=config['train']['mode'], type=str)
+    args.add_argument('-loss_func', default=config['train']['loss_func'], type=str)
+    args.add_argument('-seed', default=config['train']['seed'], type=int)
+    args.add_argument('-batch_size', default=config['train']['batch_size'], type=int)
+    args.add_argument('-lr_init', default=config['train']['lr_init'], type=float)
+    args.add_argument('-lr_decay', default=config['train']['lr_decay'], type=eval)
+    args.add_argument('-lr_decay_rate', default=config['train']['lr_decay_rate'], type=float)
+    args.add_argument('-lr_decay_step', default=config['train']['lr_decay_step'], type=str)
+    args.add_argument('-early_stop', default=config['train']['early_stop'], type=eval)
+    args.add_argument('-early_stop_patience', default=config['train']['early_stop_patience'], type=int)
+    args.add_argument('-grad_norm', default=config['train']['grad_norm'], type=eval)
+    args.add_argument('-max_grad_norm', default=config['train']['max_grad_norm'], type=int)
+    args.add_argument('-real_value', default=config['train']['real_value'], type=eval,
+                      help='use real value for loss calculation')
+    args.add_argument('-pretrain_epochs', default=config['train']['pretrain_epochs'], type=int)
+    args.add_argument('-eval_epochs', default=config['train']['eval_epochs'], type=int)
+    args.add_argument('-ori_epochs', default=config['train']['ori_epochs'], type=int)
+    args.add_argument('-load_pretrain_path', default=config['train']['load_pretrain_path'], type=str)
+    args.add_argument('-save_pretrain_path', default=config['train']['save_pretrain_path'], type=str)
+    args.add_argument('-debug', default=config['train']['debug'], type=str)
+    # test
+    args.add_argument('-mae_thresh', default=config['test']['mae_thresh'], type=eval)
+    args.add_argument('-mape_thresh', default=config['test']['mape_thresh'], type=float)
+    # log
+    args.add_argument('-log_dir', default='./', type=str)
+    args.add_argument('-log_step', default=config['log']['log_step'], type=int)
+    args.add_argument('-plot', default=config['log']['plot'], type=eval)
+    args, _ = args.parse_known_args()
+    return args

conf/FlashST/config.conf

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+[new_para]
+# dataset_use = ['PEMS08', 'PEMS04', 'PEMS07', 'PEMS03']
+[data]
+dataset_use = PEMS08,PEMS04,PEMS07,PEMS03
+# dataset_use = PEMS08,PEMS04
+dataset_test = PEMS07M   # NYC_BIKE, CA_District5, PEMS07M, chengdu_didi
+his = 12
+pred = 12
+val_ratio = 0.2
+test_ratio = 0.2
+tod = False
+normalizer = std
+column_wise = False
+default_graph = True
+
+[model]
+input_base_dim = 1
+input_extra_dim = 2
+output_dim = 1
+use_lpls = False
+node_dim = 32
+embed_dim = 32
+num_layer = 3
+temp_dim_tid = 32
+temp_dim_diw = 32
+if_time_in_day = True
+if_day_in_week = True
+if_spatial = True
+
+
+[train]
+loss_func = mask_mae
+seed = 0
+batch_size = 64
+lr_init = 0.003
+lr_decay = True
+lr_decay_rate = 0.3
+lr_decay_step = 70, 160, 240
+early_stop = True
+early_stop_patience = 25
+grad_norm = True
+max_grad_norm = 5
+real_value = False
+pretrain_epochs = 300
+eval_epochs = 20
+ori_epochs = 100
+load_pretrain_path = GWN_P8437.pth
+save_pretrain_path = P8437_stgcn.pth
+debug = True
+
+[test]
+mae_thresh = 0.
+mape_thresh = 0.001
+
+
+[log]
+log_step = 20
+plot = False

conf/GWN/CA_District5.conf

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+[general]
+device = cuda:0
+
+[data]
+num_nodes = 211
+input_window = 12
+output_window = 12
+output_dim = 1
+
+[model]
+dropout = 0.3
+blocks = 4
+layers =  2
+gcn_bool = True
+addaptadj = True
+adjtype = doubletransition
+randomadj = True
+aptonly = True
+kernel_size = 2
+nhid = 32
+residual_channels = 32
+dilation_channels = 32

conf/GWN/NYC_BIKE.conf

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+[general]
+device = cuda:0
+
+[data]
+num_nodes = 250
+input_window = 12
+output_window = 12
+output_dim = 1
+
+[model]
+dropout = 0.3
+blocks = 4
+layers =  2
+gcn_bool = True
+addaptadj = True
+adjtype = doubletransition
+randomadj = True
+aptonly = True
+kernel_size = 2
+nhid = 32
+residual_channels = 32
+dilation_channels = 32